000 | 03323nam a22005295i 4500 | ||
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001 | 978-3-662-48395-4 | ||
003 | DE-He213 | ||
005 | 20200420220223.0 | ||
007 | cr nn 008mamaa | ||
008 | 160504s2016 gw | s |||| 0|eng d | ||
020 |
_a9783662483954 _9978-3-662-48395-4 |
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024 | 7 |
_a10.1007/978-3-662-48395-4 _2doi |
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050 | 4 | _aQA75.5-76.95 | |
072 | 7 |
_aUY _2bicssc |
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_aUYA _2bicssc |
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_aCOM014000 _2bisacsh |
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_aCOM031000 _2bisacsh |
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082 | 0 | 4 |
_a004.0151 _223 |
245 | 1 | 0 |
_aTopics in Grammatical Inference _h[electronic resource] / _cedited by Jeffrey Heinz, Jos�e M. Sempere. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2016. |
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300 |
_aXVII, 247 p. 56 illus., 7 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aIntroduction -- Gold-Style Learning Theory -- Efficiency in the Identification in the Limit Learning Paradigm -- Learning Grammars and Automata with Queries -- On the Inference of Finite State Automata from Positive and Negative Data -- Learning Probability Distributions Generated by Finite-State Machines -- Distributional Learning of Context-Free and Multiple -- Context-Free Grammars -- Learning Tree Languages -- Learning the Language of Biological Sequences. | |
520 | _aThis book explains advanced theoretical and application-related issues in grammatical inference, a research area inside the inductive inference paradigm for machine learning. The first three chapters of the book deal with issues regarding theoretical learning frameworks; the next four chapters focus on the main classes of formal languages according to Chomsky's hierarchy, in particular regular and context-free languages; and the final chapter addresses the processing of biosequences. The topics chosen are of foundational interest with relatively mature and established results, algorithms and conclusions. The book will be of value to researchers and graduate students in areas such as theoretical computer science, machine learning, computational linguistics, bioinformatics, and cognitive psychology who are engaged with the study of learning, especially of the structure underlying the concept to be learned. Some knowledge of mathematics and theoretical computer science, including formal language theory, automata theory, formal grammars, and algorithmics, is a prerequisite for reading this book. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aComputers. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aBioinformatics. | |
650 | 0 | _aComputational linguistics. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aTheory of Computation. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aComputational Linguistics. |
650 | 2 | 4 | _aComputational Biology/Bioinformatics. |
700 | 1 |
_aHeinz, Jeffrey. _eeditor. |
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700 | 1 |
_aSempere, Jos�e M. _eeditor. |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783662483930 |
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-662-48395-4 |
912 | _aZDB-2-SCS | ||
942 | _cEBK | ||
999 |
_c52043 _d52043 |